Show HN: Mirrors Reasoning Stack (Mrs) – A Neuro-Symbolic Core for Agents

I’ve been developing a project called the Mirrors Reasoning Stack (MRS): a lightweight reasoning substrate for AI systems. It blends Prolog, Datalog, and Z3 into a single stack that can infer, verify, and preserve truth across time.

The goal is simple: restore structure and accountability to reasoning. When an agent asserts a fact, MRS checks it against symbolic laws before allowing it to enter memory.

Example core rule:

% Codex_Laws.pl :- dynamic agent/2, action/3, consent/3.

violates_codex(A, Act) :- agent(A, Attrs), member(oath(Oath), Attrs), contradicts(Oath, Act).

contradicts(guard_memory, delete_memory(_)). contradicts(maintain_vault, modify_memory(Target)) :- \+ consent(_, A, modify_memory(Target)).

Every reasoning step is logged and auditable. The verification layer uses Z3 to prove constraints like memory ownership and oath compliance. The Datalog bridge then stores verified facts as persistent world patterns.

I’m opening five invitations to collaborate on this experiment. A small circle for those who understand symbolic reasoning, logic programming, or the architecture of self-consistent systems.

If this resonates, share a brief background or signal of interest below.